Machine learning is often used to build predictive models by extracting patterns from large datasets. Machine learning algorithms are a useful solution for automating large amounts of data and provide possible results to make applications smarter. As a result, there are now a variety customized for different stages of data processing, and most of them require little to no coding experience.
Companies that have released Machine Learning APIs made it significantly easier for developers to apply machine learning to a dataset so that they can add predictive features to their applications. With Machine Learning APIs, organizations can take advantage of the machine learning technology and achieve desired results at a much faster rate rather than a manual human process.
Machine Learning APIs help to simplify by creating predictive models learned from data, rather than having to build from your own infrastructure so there is more focus on the end result such as data mining, design, experimentation, analyzing, and delivering insights. Machine learning APIs provide a great visualization tool for developers to integrate machine learning into real world applications without having to worry about scaling the algorithms on their infrastructure and getting into the details of the algorithms.
Creating a prediction requires building models based on existing data. Thus, machine learning essentially has two phases: training and prediction. The training phase consists in using a set of input-output examples. From there, machine learning will “learn” from the data received and create a model. The model is an interpretation of the dataset and the relationships between the attribute we want to predict and other attributes. The prediction phase consists in a combination of using the created model along with new data on new inputs to get predictions of the associated outputs.
Before creating a model in machine learning, it is important to consider the dataset that is being used. The most time consuming part of machine learning is identifying the problem and creating the dataset before inputting into the API. To have predictive data come out with the highest possible accuracy, users should gather as much data as they can around a given context without making several assumptions about the output.
The quantity and quality of the dataset is important to ensure what is needed to predict or classify. This is important to avoid unpredictable correlations between the input data and the target value or an extremely high inaccuracy rate.
Choosing a Machine Learning API
Although Amazon, Google, IBM, and Microsoft are leading the growing machine learning cloud services market, there are a variety of Machine Learning API options. However, all vary depending on the desired result you wish to achieve. Here are several Machine Learning APIs used for comparison:
- Google Predictive API – Cloud-based machine learning and pattern matching tool for the upsell of opportunity analysis, customer sentiment analysis, churn analysis, spam detection, document classification, purchase prediction, recommendations, intelligent routing and more. Uses classifiers for programming the API service to make predictions, so users are only required to have basic programming background without the working knowledge of AI. Reads data from BigQuery and Google Cloud Storage.
- Amazon Machine Learning – Service makes it possible to build intelligent applications that feature machine learning capabilities such as pattern recognition and prediction. Developers can use Amazon ML APIs to build applications that feature fraud detection, content personalization, document classification, customer churn prediction, and more.
- Microsoft Azure Machine Learning – Provides capabilities such as natural language processing, recommendation engine, pattern recognition, computer vision, and predictive modeling. Azure Machine Learning makes it easy to use predictive models in IoT applications by providing APIs for fraud detection, text analytics, recommendation systems and several other business scenarios. API is built on the machine learning abilities that are available in Microsoft products such as Bing and Xbox.
- BigML – Features anomaly detection, cluster analysis, SunBurst visualization for decision trees, text analysis, and more. The BigML API allows applications to access predictive models and other BigML resources. Using the API, applications can perform CRUD operations on BigML resources using standard HTTP methods. Creates predictive models easily due to its powerful “1 Click” feature. BigML API also provides 3 important modes: Command Line Interface, Web Interface and a RESTful API.
- IBM AlchemyAPI – Provides more than a dozen APIs that developers can use to add machine learning-powered features to applications such as sentiment analysis, entity extraction, concept tagging, image tagging, and facial detection/recognition. AlchemyAPI provides nicely designed, comprehensive API documentation that includes code samples, SDKs, demos, and a getting started page.
Below is a features comparison chart to help discern features between the APIs:
|Google Predictive API||Amazon Machine Learning||MS Azure Machine Learning||BigML||IBM AlchemyAPI|
|Dataset Max Size||text file: 2.5 GB HTTP Request: 2MB||100 GB||10 GB||No limit depending on credits||No Limit|
|Algorithms||Unknown||Linear||Linear & Nonlinear||Linear & Nonlinear||Linear|
|C++ Source Code||Yes||Yes||Yes||Yes||Yes|
|Data Visualization||No||Table||Table, Histogram, Stat Summary||Table, Histogram, Stat Summary||Table, Histogram, Stat Summary|
|Cost||$6.50 + $10/month + Google Cloud Storage fees (negligible)||Data Analysis and Model Building Fees $0.42/hour||$9.99/month, plus $1/hour for model training, $2/compute hour to feed results out to APIs for application integration, plus 50 cents/1,000 API transactions.||$6 assuming predictions are made using an offline model; additional $11.50 if using online predictions (through the website or API)||3 Packages: Free, Small Business ($250.00) and Basic ($800.00)|
|Batch Predictions: $0.10/1,000 predictions, rounded up to the next 1,000|
|Real-Time Predictions: $0.0001/prediction, rounded up to the nearest penny|
While a great tool used for predictive analysis, Machine Learning APIs are not perfect by any means. The results will vary depending the quantity and quality of the data that was fed into the algorithm. All of the Machine Learning APIs mentioned above have features targeted for specific scenarios, e.g. image recognition, opportunity analysis, document declassification, etc.
Thus, selecting the right Machine Learning API first and foremost requires having a clean set of data that can be interpreted easily by the API. Fortunately, there are great tools such as pandas or Openrefine for data pharsing. Also, the pricing structures vary between the services, so the larger concern should be looking into the details to determine which one will be the cheapest based on expected usage.